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Abstract: The results of empirical comparisons of existing learning algorithms illustrate that each algorithm has a selective superiority; it is best for some but not all tasks. Given a data set, it is often not clear beforehand which algorithm will yield the best performance. In such cases one must search the space of available algorithms to find the one that produces the best classifier. In this paper we present an approach that applies knowledge about the representational biases of a set of learning... (Update)
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BibTeX entry: (Update)
Brodley, C.E. (1993), Addressing the Selective Superiority Problem: Automatic Algorithm /Model Class Selection, in Proceedings of the Tenth International Conference on Machine Learning, pp. 17-24. http://citeseer.ist.psu.edu/brodley93addressing.html More
@misc{ brodley93addressing,
author = "C. Brodley",
title = "Addressing the Selective Superiority Problem: Automatic Algorithm /Model
Class Selection",
text = "Brodley, C.E. (1993), Addressing the Selective Superiority Problem: Automatic
Algorithm /Model Class Selection, in Proceedings of the Tenth International
Conference on Machine Learning, pp. 17-24.",
year = "1993",
url = "citeseer.ist.psu.edu/brodley93addressing.html" }
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